One approach used for encoding a digital image is provided by Joint Photographic Experts Group (JPEG), which transforms the image from one form of information, e.g., spatial information, into another form, e.g., frequency information. The transformed data undergoes a quantization step before encoding to generate a compressed image. In the transformation operation, the image may be broken up into small squares of pixels, e.g., an 8×8 pixel square, with each square being input to a transform, e.g., a discrete cosine transform (DCT) or fast fourier transform (FFT), to generate a set of coefficients, e.g., a set of 64 coefficients, comprising both high frequency and low frequency coefficients. In quantization, a quantization matrix is applied to each square's set of coefficients, e.g., DCT coefficients, which may, depending on the quantization matrix used, result in some of the non-zero-valued coefficients being set to zero. Each quantization matrix may be considered to provide a corresponding image quality, and tradeoffs exist, e.g., a tradeoff between image quality and amount of storage space saved using a given quantization matrix. During encoding, or coding, the coefficients in each quantized matrix are converted to a stream of binary data, which may be further compressed using a compression algorithm, e.g., each run of zeroes and ones may be consolidated. Consequently, a quantization matrix that results in a greater number of zero coefficients may result in a greater amount of compression than a quantization matrix that results in fewer zeroed-out coefficients. However, the former quantization matrix may result in a less desirable picture quality than the latter.